PROJECT SUMMARY The expectation that enhancing diversity of the biomedical Big Data workforce will improve its practice is the driving force for RFA-MD-16-002 that calls for development of both research experiences and curriculum in data science. However, curriculum that is highly mathematical and/or computational can trigger stereotype threat and underperformance in students from underrepresented groups, and poor research experiences can cause exit from science. Therefore, the overall goal of our HEART & SOUL program is to use a systemic approach to creating intellectually safe research and teaching environments for all students interested in biomedical Big Data science. This will be accomplished at San Francisco State University (SF State) by engaging faculty participants in mentored research experiences that will hone both their skills in participatory technologies for Big Data research that is inclusive of minority populations, and in mentoring for diversity. Furthermore, faculty participants will benefit from mentoring and networking efforts with faculty at our research partner, the University of California, San Francisco (UCSF). Consequently, the HEART & SOUL program partners SF State, a predominantly undergraduate institution that has garnered a national reputation for excellence in preparing underrepresented students for biomedical research careers, with UCSF that is a research-intensive institution with prominence in biomedical big data research. Working together these institutions will develop the faculty change agents necessary to create affirming research environments for undergraduates participating in Big Data research experiences, and to implement effective instructional approaches for engaging additional undergraduates in this field. The latter activity will benefit from SF State?s commitment to effective scientific teaching and active learning in science. Taken together our activities are expected to result in the sustainable infrastructure for research training and teaching in biomedical Big Data science to make data science accessible and ensure a new cadre of students have the skills necessary to build participatory technologies employed by a diverse workforce.